Abstract
Abstract. Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.
Highlights
Crop yield forecasting is one of the main components of agriculture monitoring and an extremely important input in enabling food security and sustainable development (Kussul et al, 2011, 2010b; Skakun et al, 2014, 2015)
The following satellite-based predictors for empirical regression crop yield models are used in the study: 16-day Normalized Difference Vegetation Index (NDVI) composites derived from Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m spatial resolution, leaf area index (LAI) and FAPAR composites from SPOT-Vegetation at 1 km spatial resolution
In the paper the problem of winter wheat crop yield prediction is considered at different scales, namely NUTS2, NUTS3 and field level for the territory of Kirovohrad region of Ukraine
Summary
Crop yield forecasting is one of the main components of agriculture monitoring and an extremely important input in enabling food security and sustainable development (Kussul et al, 2011, 2010b; Skakun et al, 2014, 2015). They find that performance of empirical regression models based on satellite data with biophysical variables (such as FAPAR) is approximately 20% more accurate comparing to the NDVI approach when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest. We have estimated efficiency of using predictors of different nature (vegetation indices, biophysical parameters, and a crop growth model adopted for the territory of Ukraine) at oblast level (Kogan et al, 2013a, 2013b; Kussul et al, 2013; Kussul et al, 2014). The objective of the study presented in this paper is to assess the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field)
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